Machine Learning Engineer Manager

KAYAK, part of Booking Holdings (NASDAQ: BKNG), is the world's leading travel search engine. With billions of queries across our platforms, we help people find their perfect flight, stay, rental car and vacation package. We're also transforming business travel with a new corporate travel solution, KAYAK for Business.

As an employee of KAYAK, you will be part of a travel company that operates a portfolio of global metasearch brands including momondo, Cheapflights and HotelsCombined, among others. From start-up to industry leader, innovation is at our core and every employee has an opportunity to make their mark. Our focus is on building the best travel search engine to make it easier for everyone to experience the world.

KAYAK is seeking a Machine Learning Engineering Manager to lead the development and evolution of its shared Machine Learning Platform. This is a senior, hands-on technical leadership role.

You’ll lead a Berlin-based team which builds the shared infrastructure and developer workflows that take models from experimentation to reliable production. You’ll own its technical direction, roadmap, and delivery while staying hands-on with architecture and the hardest engineering problems. In partnership with the team, you’ll build self-service capabilities for training, deployment, serving, and monitoring so KAYAK teams can ship models safely and at scale.

If you’re excited to combine people leadership with building the systems that make machine learning work in production, we’d love to hear from you.

This role will be required to work from our Berlin office 3 days per week.

In this role, you will:

• Own the strategy and roadmap for KAYAK’s shared Machine Learning Platform, aligning technical investments with business needs and long-term platform evolution.

• Lead, mentor, and grow a high-performing Berlin-based team of Machine Learning Platform Engineers, fostering technical excellence, autonomy, and operational accountability.

• Drive technical direction across platform architecture and the ML lifecycle, contributing hands-on to high-impact problems and simplifying legacy systems where it creates clear value.

• Build and evolve shared ML capabilities, including training orchestration, model serving, feature management, and LLM workflows, improving developer experience and enabling teams to deliver production-ready ML solutions efficiently.

• Champion platform reliability and operational excellence, establishing robust monitoring, alerting, incident response, and production-readiness standards.

• Define clear ownership boundaries so the platform team owns shared infrastructure and lifecycle tooling, while downstream teams own use-case-specific model and business logic.